The environment of low-altitude urban airspace is complex and variable due to numerous obstacles, non-cooperative aircrafts, and birds. Unmanned aerial vehicles (UAVs) leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security. However, the timely information of surrounding situation is difficult to acquire by UAVs, which further brings security risks. As a mature technology leveraged in traditional civil aviation, the automatic dependent surveillance-broadcast (ADS-B) realizes continuous surveillance of the information of aircrafts. Consequently, we leverage ADS-B for surveillance and information broadcasting, and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning. In detail, we propose the secure sub-airspaces planning (SSP) algorithm and particle swarm optimization rapidly-exploring random trees (PSO-RRT) algorithm for the UAV trajectory planning in law-altitude airspace. The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory, and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.
We present a solution to the problem of fairly planning a fleet of Unmanned Aerial Vehicles (UAVs) that have different missions and operators, such that no one operator unfairly gets to finish its missions early at the expense of others - unless this was explicitly negotiated. When hundreds of UAVs share an urban airspace, the relevant authorities should allocate corridors to them such that they complete their missions, but no one vehicle is accidentally given an exceptionally fast path at the expense of another, which is thus forced to wait and waste energy. Our solution, FairFly, addresses t…
The National Airspace System (NAS) is a complex and evolving system that enables safe and efficient aviation. Advanced air mobility concepts and new airspace entrants, such as unmanned aircraft, must integrate into the NAS without degrading overall safety or efficiency. For instance, regulations, standards, and systems are required to mitigate the risk of a midair collision between aircraft. Monte Carlo simulations have been a foundational capability for decades to develop, assess, and certify aircraft conflict avoidance systems. These are often validated through human-in-the-loop experiments …
School of Information Science and Technology, Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳）信息科学与技术学院）；Guangdong Provincial Key Laboratory of Space-Aerial Networking and Intelligent Sensing（广东省空间-航空网络与智能感知重点实验室）；Information Systems Technology and Design, Singapore University of Technology and Design（新加坡科技设计大学信息系统技术与设计）；School of System Design and Intelligent Manufacturing, Southern University of Science and Technology（南方科技大学系统设计与智能制造学院）
This paper addresses the problem of restricted airspace protection from invaders using the cooperative multi-UAV system. The objective is to detect and capture the invaders cooperatively by a team of homogeneous UAVs (called evaders)before invaders enter the restricted airspace. The problem of restricted airspace protection problem is formulated as a Multi-UAV Spatio-Temporal Multi-Task Allocation problem and is referred as MUST-MTA. The MUST-MTA problem is solved using a modified consensus-based bundled auction method. Here, the spatial and time constraints are handled by combining both spati…
This paper proposes a physics-inspired solution for low altitude Unmanned Aircraft System (UAS) Traffic Management (UTM) in urban areas. We decompose UTM into spatial and temporal planning problems. For the spatial planning problem, we use the principles of Eulerian continuum mechanics to safely and optimally allocate finite airspace to a UAS. To this end, the finite airspace is partitioned into planned and unplanned subspaces with unplanned subspace(s) or zone(s) enclosing buildings and restricted no-fly regions. The planned subspace is divided into navigable channels that safely wrap unplann…
We consider multiple unmanned aerial vehicles (UAVs) at a common altitude serving as data collectors to a network of IoT devices. First, using a probabilistic line of sight channel model, the optimal assignment of IoT devices to the UAVs is determined. Next, for the asymptotic regimes of a large number of UAVs and/or large UAV altitudes, we propose closed-form analytical expressions for the optimal data rate and characterize the corresponding optimal UAV deployments. We also propose a simple iterative algorithm to find the optimal deployments with a small number of UAVs at high altitudes. Glob…
This letter studies the signal-to-interference-plus-noise (SINR) meta distribution of uplink transmission of UAV-enabled wireless networks with inversion power control. Within a framework of stochastic geometry, the Matern cluster process (MCP) is used to model the locations of users and UAVs. Conditional success probability and moments are derived to compute the exact expression and moment matching approximation of SINR meta distribution (beta approximation). Specifically, the effect of the power control compensation factor and UAV altitude are studied. Our numerical results show that UAV alt…
Urban Air Mobility (UAM), or the scenario where multiple manned and Unmanned Aerial Vehicles (UAVs) carry out various tasks over urban airspaces, is a transportation concept of the future that is gaining prominence. UAM missions with complex spatial, temporal and reactive requirements can be succinctly represented using Signal Temporal Logic (STL), a behavioral specification language. However, planning and control of systems with STL specifications is computationally intensive, usually resulting in planning approaches that do not guarantee dynamical feasibility, or control approaches that cann…
From Ground to Sky: Architectures, Applications, and Challenges Shaping Low-Altitude Wireless Networks
Southern University of Science and Technology（南方科技大学）；Southeast University（东南大学）；Nanyang Technological University（南洋理工大学）；Jilin University（吉林大学）；Chongqing University（重庆大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Koc University（科奇大学）；Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR)（信息通信研究所，科技研究局）；Auburn University（奥本大学）；University of Sydney（悉尼大学）；Tohoku University（东北大学）；Sungkyunkwan University（成均馆大学）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
City-Wide Low-Altitude Urban Air Mobility: A Scalable Global Path Planning Approach via Risk-Aware Multi-Scale Cell Decomposition
School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）；School of Aeronautics and Astronautics, Purdue University（普渡大学航空航天学院）
